import numpy as np
import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
pd.__version__
'1.4.1'
#Do this if the below line gives some error ~ AttributeError: Can't get attribute '_unpickle_block' on <module 'pandas._libs.internals' .....
#%pip install pandas==1.4.1
# from google.colab import drive
# drive.mount('/content/drive')
pd.__version__
'1.4.1'
import pickle
with open('train100c5k_v2.pkl', 'rb') as file:
data = pd.read_pickle(file)
data
| data | target | |
|---|---|---|
| 0 | [[46, 132, 11, 1, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0... | 0 |
| 1 | [[0, 0, 3, 0, 116, 78, 0, 1, 1, 0, 0, 0, 0, 0,... | 0 |
| 2 | [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4,... | 0 |
| 3 | [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,... | 0 |
| 4 | [[0, 0, 0, 69, 165, 202, 199, 214, 204, 170, 1... | 0 |
| ... | ... | ... |
| 499995 | [[0, 0, 0, 0, 1, 4, 7, 8, 6, 4, 5, 1, 0, 0, 0,... | 99 |
| 499996 | [[0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0,... | 99 |
| 499997 | [[0, 0, 0, 0, 2, 0, 25, 26, 0, 1, 0, 0, 0, 0, ... | 99 |
| 499998 | [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,... | 99 |
| 499999 | [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,... | 99 |
500000 rows × 2 columns
#Plotting Image
# plt.figure(figsize=(10,10))
# a = 0
# for i in range(1, 500000, 5000):
# plt.subplot(10, 10, a+1)
# plt.xticks([])
# plt.yticks([])
# plt.imshow(data["data"][i])
# a+=1
# plt.show()
# Specifying the fraction of items to return in the sample (Set to 1, to return all the data)
df = data.sample(frac = 1)
df
| data | target | |
|---|---|---|
| 210595 | [[2, 0, 0, 14, 57, 90, 90, 59, 25, 6, 0, 0, 0,... | 42 |
| 2641 | [[0, 30, 180, 52, 0, 22, 35, 47, 29, 0, 1, 0, ... | 0 |
| 48490 | [[0, 0, 0, 0, 3, 0, 32, 130, 194, 209, 165, 39... | 9 |
| 164767 | [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,... | 32 |
| 384528 | [[0, 0, 0, 1, 6, 4, 0, 0, 0, 54, 61, 0, 0, 0, ... | 76 |
| ... | ... | ... |
| 267649 | [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 19, 87, 99,... | 53 |
| 186157 | [[0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 15, 49, 57, 63... | 37 |
| 59118 | [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3,... | 11 |
| 113265 | [[0, 0, 0, 3, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,... | 22 |
| 490267 | [[0, 3, 20, 50, 86, 110, 120, 118, 113, 114, 1... | 98 |
500000 rows × 2 columns
df.describe()
| target | |
|---|---|
| count | 500000.000000 |
| mean | 49.500000 |
| std | 28.866099 |
| min | 0.000000 |
| 25% | 24.750000 |
| 50% | 49.500000 |
| 75% | 74.250000 |
| max | 99.000000 |
df.isna().count()
data 500000 target 500000 dtype: int64
type(df['data'].values)
numpy.ndarray
from sklearn.model_selection import train_test_split
train_data, test_data = train_test_split(df, test_size=0.2, random_state=10) #Change this to test accuracy
train_data.head(100)
| data | target | |
|---|---|---|
| 263244 | [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 13, 2... | 52 |
| 39992 | [[0, 0, 1, 1, 2, 2, 2, 1, 1, 0, 0, 0, 0, 0, 1,... | 7 |
| 283325 | [[0, 0, 0, 0, 0, 0, 0, 2, 0, 1, 50, 101, 114, ... | 56 |
| 97652 | [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,... | 19 |
| 197661 | [[0, 6, 0, 0, 0, 0, 29, 45, 0, 0, 0, 0, 0, 0, ... | 39 |
| ... | ... | ... |
| 355991 | [[0, 187, 94, 6, 9, 8, 1, 64, 138, 147, 126, 7... | 71 |
| 248567 | [[0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,... | 49 |
| 178516 | [[0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 27, 54, 77,... | 35 |
| 20657 | [[0, 0, 0, 0, 0, 1, 3, 4, 5, 6, 3, 0, 5, 63, 8... | 4 |
| 191811 | [[0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 1, 2, 1,... | 38 |
100 rows × 2 columns
train_images_data = train_data['data'].values
train_target_data = train_data['target'].values
#Checking if the size of train_images and train_labels is same
print(f"train_images_data.shape = {train_images_data.shape}")
print(f"type(train_images_data) = {type(train_images_data)}")
print(f"train_target_data.shape = {train_target_data.shape}")
print(f"type(train_target_data) = {type(train_target_data)}")
train_images_data.shape = (400000,) type(train_images_data) = <class 'numpy.ndarray'> train_target_data.shape = (400000,) type(train_target_data) = <class 'numpy.ndarray'>
train_data = [x.reshape(28, 28, ) for x in train_images_data]
train_data
[array([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0,
13, 22, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 0, 16, 128,
227, 239, 215, 170, 90, 4, 0, 5, 2, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 5, 1, 0, 85, 226, 254,
185, 141, 178, 227, 253, 207, 94, 0, 0, 5, 4, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 2, 8, 0, 14, 146, 247, 218, 78,
23, 10, 24, 42, 107, 219, 254, 196, 87, 0, 0, 4, 0,
0, 0],
[ 0, 0, 0, 0, 4, 0, 0, 61, 201, 255, 159, 40, 0,
0, 0, 0, 0, 0, 43, 120, 223, 253, 186, 82, 1, 3,
0, 0],
[ 0, 0, 0, 8, 0, 22, 125, 225, 238, 107, 16, 0, 2,
3, 2, 2, 3, 0, 0, 0, 51, 131, 228, 240, 118, 0,
3, 0],
[ 0, 0, 4, 0, 42, 169, 255, 213, 65, 0, 0, 5, 0,
0, 0, 0, 0, 1, 4, 1, 0, 0, 58, 195, 245, 48,
2, 1],
[ 0, 2, 0, 71, 204, 227, 236, 170, 4, 3, 5, 0, 0,
0, 0, 0, 0, 0, 0, 1, 5, 3, 0, 58, 238, 174,
0, 1],
[ 0, 0, 60, 236, 225, 82, 161, 235, 72, 1, 4, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 13, 158, 244,
19, 1],
[ 1, 10, 199, 234, 68, 0, 92, 241, 123, 0, 6, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 94, 251,
68, 0],
[ 0, 102, 253, 112, 8, 5, 43, 223, 191, 1, 5, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 1, 61, 252,
110, 0],
[ 0, 213, 212, 29, 1, 2, 0, 150, 181, 4, 1, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 43, 225,
158, 0],
[ 14, 238, 140, 10, 0, 3, 1, 9, 12, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 27, 189,
206, 0],
[ 52, 244, 108, 3, 0, 0, 0, 0, 0, 0, 4, 2, 0,
0, 0, 0, 1, 1, 1, 1, 0, 0, 2, 0, 11, 150,
238, 13],
[ 90, 243, 166, 81, 35, 1, 0, 0, 0, 59, 183, 116, 36,
47, 62, 67, 76, 86, 94, 85, 70, 60, 53, 36, 25, 116,
244, 58],
[ 89, 229, 255, 254, 248, 231, 232, 241, 242, 247, 255, 251, 249,
249, 242, 249, 255, 255, 255, 255, 254, 253, 251, 249, 245, 251,
247, 106],
[ 11, 29, 41, 77, 125, 165, 168, 160, 141, 166, 242, 199, 104,
133, 255, 164, 80, 64, 62, 68, 83, 94, 103, 114, 128, 139,
124, 36],
[ 0, 0, 0, 0, 6, 18, 18, 19, 10, 61, 222, 155, 0,
36, 252, 115, 0, 0, 0, 0, 0, 0, 0, 3, 7, 11,
12, 0],
[ 1, 3, 3, 0, 0, 0, 0, 0, 0, 45, 216, 160, 0,
26, 252, 124, 7, 1, 2, 1, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 1, 1, 2, 2, 6, 2, 48, 213, 169, 1,
24, 252, 130, 8, 0, 1, 0, 1, 1, 1, 1, 1, 2,
2, 0],
[ 0, 0, 0, 0, 0, 0, 0, 4, 0, 42, 206, 181, 0,
17, 251, 136, 9, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 4, 0, 48, 214, 172, 0,
12, 251, 141, 9, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 5, 0, 62, 239, 133, 4,
9, 248, 148, 11, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 4, 0, 86, 253, 87, 0,
4, 245, 152, 12, 0, 2, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 1, 2, 0, 104, 255, 143, 43,
52, 252, 123, 7, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 2, 0, 61, 215, 247, 236,
201, 255, 55, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 77, 177,
235, 200, 14, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0,
22, 16, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0]], dtype=uint8),
array([[ 0, 0, 1, 1, 2, 2, 2, 1, 1, 0, 0, 0, 0,
0, 1, 1, 0, 6, 39, 84, 106, 105, 79, 30, 0, 0,
1, 0],
[ 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,
0, 2, 1, 53, 169, 237, 248, 253, 253, 247, 235, 159, 38,
0, 2],
[ 0, 25, 94, 153, 202, 228, 222, 157, 66, 9, 1, 0, 4,
0, 0, 106, 232, 255, 188, 89, 51, 54, 98, 202, 255, 225,
88, 3],
[ 23, 118, 237, 252, 198, 180, 208, 251, 222, 99, 5, 1, 1,
11, 74, 212, 255, 125, 24, 0, 0, 0, 0, 36, 151, 255,
160, 44],
[112, 201, 247, 114, 35, 22, 34, 137, 255, 195, 64, 12, 7,
33, 185, 250, 170, 36, 0, 3, 6, 2, 0, 0, 93, 251,
175, 62],
[173, 212, 190, 30, 0, 0, 0, 53, 206, 243, 140, 5, 0,
47, 220, 234, 101, 0, 0, 0, 4, 2, 2, 2, 92, 251,
178, 66],
[168, 202, 184, 45, 3, 5, 8, 29, 117, 217, 221, 167, 151,
204, 255, 255, 205, 155, 106, 46, 1, 0, 3, 3, 96, 251,
179, 69],
[148, 203, 203, 49, 4, 0, 0, 19, 127, 235, 255, 255, 216,
193, 187, 195, 230, 255, 255, 197, 83, 7, 0, 25, 150, 255,
146, 29],
[125, 203, 224, 53, 0, 1, 52, 151, 245, 234, 164, 88, 33,
14, 30, 45, 66, 111, 185, 249, 229, 112, 28, 101, 226, 242,
93, 0],
[ 64, 182, 255, 124, 49, 109, 222, 255, 198, 90, 8, 0, 0,
0, 0, 0, 0, 0, 17, 110, 229, 236, 166, 206, 243, 155,
41, 2],
[ 5, 105, 227, 224, 221, 255, 227, 123, 12, 0, 2, 5, 4,
2, 2, 0, 0, 8, 1, 0, 75, 188, 255, 255, 190, 46,
0, 3],
[ 1, 12, 95, 218, 255, 245, 87, 0, 11, 11, 3, 0, 0,
0, 1, 12, 17, 4, 3, 4, 0, 61, 202, 251, 163, 21,
0, 6],
[ 0, 23, 118, 229, 246, 182, 48, 37, 128, 104, 34, 4, 0,
1, 19, 122, 166, 52, 0, 0, 3, 8, 67, 214, 247, 111,
13, 0],
[ 1, 102, 254, 236, 112, 0, 0, 82, 237, 215, 92, 9, 0,
5, 39, 182, 251, 117, 8, 0, 4, 0, 0, 136, 248, 214,
72, 0],
[ 65, 173, 255, 121, 15, 0, 0, 73, 223, 219, 105, 11, 0,
0, 1, 145, 255, 160, 40, 0, 0, 0, 0, 48, 168, 255,
143, 23],
[148, 209, 212, 52, 0, 2, 0, 14, 64, 57, 18, 0, 1,
4, 3, 118, 245, 185, 67, 0, 0, 6, 1, 1, 71, 232,
209, 132],
[187, 209, 173, 41, 0, 5, 94, 171, 194, 203, 189, 143, 51,
0, 0, 0, 26, 28, 13, 0, 0, 3, 1, 0, 36, 173,
210, 190],
[208, 208, 151, 33, 0, 52, 191, 254, 255, 255, 255, 255, 217,
176, 178, 202, 170, 60, 0, 0, 0, 3, 1, 0, 33, 156,
207, 201],
[198, 209, 160, 37, 0, 49, 205, 255, 249, 157, 92, 106, 174,
221, 211, 234, 255, 216, 105, 2, 0, 7, 1, 3, 35, 150,
208, 210],
[174, 205, 180, 50, 2, 13, 147, 245, 255, 223, 183, 181, 172,
114, 35, 45, 155, 255, 200, 44, 0, 5, 5, 0, 33, 190,
215, 177],
[156, 205, 195, 39, 0, 2, 40, 99, 158, 190, 209, 229, 240,
208, 155, 159, 218, 255, 166, 28, 4, 3, 0, 26, 133, 255,
186, 83],
[123, 204, 237, 91, 8, 0, 0, 0, 13, 28, 34, 56, 121,
192, 228, 227, 189, 128, 53, 0, 0, 0, 35, 156, 252, 220,
91, 10],
[ 46, 133, 249, 219, 105, 11, 0, 7, 2, 0, 0, 0, 0,
14, 45, 46, 17, 0, 0, 0, 40, 108, 196, 255, 207, 81,
10, 1],
[ 0, 26, 131, 242, 242, 128, 33, 0, 0, 0, 3, 6, 6,
0, 0, 0, 0, 16, 53, 116, 209, 255, 227, 152, 63, 0,
0, 5],
[ 7, 0, 2, 102, 221, 255, 183, 86, 40, 15, 0, 0, 0,
0, 10, 27, 62, 144, 228, 255, 222, 139, 50, 0, 0, 3,
3, 0],
[ 0, 3, 3, 0, 61, 180, 238, 255, 230, 156, 86, 53, 49,
72, 118, 189, 249, 249, 203, 115, 22, 0, 0, 5, 3, 0,
0, 0],
[ 0, 0, 0, 5, 0, 0, 38, 115, 193, 237, 250, 254, 253,
251, 243, 228, 174, 76, 8, 0, 1, 5, 2, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 1, 1, 2, 0, 0, 43, 89, 105, 111,
91, 45, 8, 0, 0, 2, 1, 0, 0, 0, 0, 0, 0,
0, 0]], dtype=uint8),
array([[ 0, 0, 0, 0, 0, 0, 0, 2, 0, 1, 50, 101, 114,
116, 119, 93, 48, 14, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 2, 0, 1, 3, 32, 104, 190, 239, 250, 255,
254, 251, 248, 235, 139, 33, 0, 0, 0, 1, 0, 0, 0,
0, 0],
[ 0, 0, 0, 3, 0, 0, 49, 182, 246, 242, 167, 88, 51,
41, 40, 119, 233, 251, 176, 54, 0, 0, 6, 0, 0, 0,
0, 0],
[ 0, 1, 1, 1, 4, 47, 150, 255, 218, 84, 18, 0, 0,
0, 0, 17, 108, 225, 248, 117, 5, 0, 2, 0, 1, 0,
0, 0],
[ 0, 3, 3, 0, 8, 128, 232, 231, 127, 5, 0, 1, 6,
3, 6, 0, 22, 168, 255, 174, 49, 6, 0, 0, 2, 0,
0, 0],
[ 1, 2, 2, 0, 32, 186, 255, 169, 62, 8, 7, 6, 6,
5, 12, 7, 6, 129, 243, 214, 109, 22, 3, 2, 4, 0,
0, 0],
[ 1, 1, 2, 2, 51, 204, 252, 130, 21, 0, 0, 0, 0,
0, 0, 0, 0, 55, 173, 245, 184, 34, 0, 0, 9, 5,
2, 0],
[ 1, 2, 5, 0, 26, 184, 249, 172, 88, 61, 56, 52, 44,
56, 97, 118, 134, 150, 181, 223, 182, 63, 18, 6, 0, 0,
0, 1],
[ 2, 0, 0, 18, 87, 214, 255, 254, 221, 219, 218, 215, 204,
230, 255, 255, 254, 240, 239, 227, 216, 203, 175, 142, 112, 74,
22, 2],
[ 4, 11, 53, 149, 230, 240, 203, 166, 153, 159, 166, 170, 172,
181, 179, 136, 80, 82, 127, 162, 182, 193, 224, 255, 255, 238,
72, 0],
[ 0, 63, 208, 255, 221, 124, 33, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 60, 210, 248, 232,
77, 0],
[ 14, 114, 245, 242, 119, 23, 0, 4, 12, 6, 6, 6, 6,
7, 7, 6, 4, 4, 5, 13, 6, 3, 74, 221, 255, 238,
97, 6],
[ 96, 162, 248, 233, 138, 46, 5, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 2, 0, 3, 97, 240, 255, 240,
119, 46],
[ 70, 137, 243, 255, 240, 180, 104, 39, 8, 3, 3, 1, 1,
0, 0, 0, 0, 3, 0, 2, 20, 99, 185, 249, 255, 243,
144, 82],
[ 87, 157, 250, 228, 180, 222, 255, 252, 207, 152, 119, 101, 80,
64, 54, 56, 61, 56, 72, 128, 202, 253, 228, 178, 217, 245,
170, 111],
[108, 182, 249, 141, 12, 18, 86, 151, 209, 243, 255, 255, 255,
252, 251, 252, 253, 250, 254, 255, 236, 161, 49, 11, 130, 244,
185, 116],
[115, 187, 245, 129, 0, 0, 6, 14, 25, 35, 44, 57, 77,
96, 106, 101, 92, 93, 90, 67, 39, 20, 0, 0, 124, 239,
188, 122],
[121, 187, 240, 130, 1, 2, 5, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 124, 233,
189, 129],
[126, 188, 236, 125, 0, 0, 8, 2, 2, 3, 3, 2, 0,
0, 0, 0, 0, 0, 0, 1, 3, 8, 0, 0, 119, 228,
190, 137],
[132, 190, 232, 122, 0, 0, 6, 1, 0, 0, 0, 0, 1,
1, 1, 1, 1, 1, 1, 0, 1, 6, 0, 0, 115, 223,
193, 144],
[137, 191, 228, 119, 0, 0, 6, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 6, 0, 0, 110, 219,
195, 151],
[143, 193, 224, 116, 0, 0, 6, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 6, 0, 0, 106, 213,
197, 159],
[148, 195, 220, 114, 2, 2, 8, 2, 1, 1, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 6, 0, 0, 101, 207,
199, 166],
[153, 194, 214, 103, 0, 0, 0, 0, 0, 0, 0, 1, 2,
3, 3, 3, 3, 3, 3, 3, 4, 9, 3, 3, 100, 203,
201, 172],
[163, 199, 216, 117, 16, 11, 15, 6, 2, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 81, 192,
201, 181],
[160, 198, 242, 217, 161, 144, 141, 123, 111, 97, 83, 67, 53,
45, 41, 41, 41, 41, 41, 41, 42, 45, 41, 41, 116, 206,
212, 195],
[ 86, 127, 195, 230, 229, 236, 239, 242, 244, 246, 249, 252, 254,
255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 252, 241,
200, 176],
[ 0, 0, 0, 0, 0, 6, 20, 33, 47, 63, 77, 91, 105,
113, 114, 114, 114, 114, 114, 114, 114, 114, 113, 113, 123, 112,
76, 54]], dtype=uint8),
array([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 4, 0, 49, 173, 189, 70, 0, 4, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
3, 0],
[ 0, 0, 0, 0, 6, 0, 86, 246, 255, 189, 32, 4, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2,
0, 2],
[ 0, 0, 0, 0, 6, 0, 93, 210, 222, 237, 123, 0, 4,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 0,
32, 0],
[ 0, 0, 0, 0, 6, 0, 95, 214, 156, 184, 220, 15, 6,
2, 1, 1, 1, 1, 2, 1, 0, 0, 2, 0, 0, 72,
157, 34],
[ 0, 0, 0, 0, 6, 0, 94, 231, 112, 87, 255, 100, 0,
0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 54, 221,
253, 110],
[ 0, 0, 0, 0, 6, 0, 94, 233, 101, 22, 225, 208, 11,
4, 2, 2, 3, 2, 3, 1, 0, 0, 0, 51, 202, 251,
238, 120],
[ 0, 0, 0, 0, 6, 0, 96, 231, 111, 0, 116, 255, 207,
99, 76, 87, 89, 87, 91, 63, 17, 0, 55, 222, 227, 142,
250, 119],
[ 0, 0, 0, 0, 6, 0, 99, 230, 113, 0, 19, 108, 226,
255, 251, 255, 255, 255, 255, 246, 224, 177, 213, 248, 75, 46,
255, 118],
[ 0, 0, 0, 0, 6, 1, 103, 228, 107, 1, 0, 5, 60,
118, 125, 112, 109, 109, 113, 138, 193, 255, 233, 93, 0, 34,
255, 118],
[ 0, 0, 0, 0, 7, 0, 94, 235, 113, 0, 8, 0, 0,
0, 0, 0, 0, 0, 0, 0, 19, 93, 79, 0, 0, 44,
255, 118],
[ 0, 0, 0, 1, 4, 9, 146, 212, 65, 1, 4, 0, 0,
21, 2, 0, 5, 8, 0, 7, 35, 0, 0, 7, 1, 41,
255, 118],
[ 0, 0, 0, 4, 0, 49, 234, 156, 0, 6, 0, 7, 62,
131, 91, 14, 1, 1, 17, 100, 192, 110, 10, 7, 0, 41,
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[ 0, 2, 0, 51, 220, 223, 61, 0, 101, 252, 171, 21, 0,
94, 254, 66, 195, 144, 17, 4, 12, 6, 0, 75, 220, 234,
223, 86],
[ 0, 3, 21, 197, 234, 76, 0, 7, 13, 132, 255, 156, 18,
65, 255, 161, 243, 107, 0, 0, 0, 0, 0, 0, 83, 255,
230, 41],
[ 1, 0, 122, 255, 108, 0, 4, 5, 0, 9, 144, 255, 146,
36, 227, 251, 251, 145, 114, 98, 68, 97, 82, 9, 56, 252,
219, 0],
[ 1, 10, 217, 199, 29, 2, 4, 0, 4, 2, 9, 157, 255,
134, 188, 254, 228, 235, 245, 252, 255, 255, 233, 139, 180, 255,
232, 26],
[ 0, 87, 253, 103, 1, 1, 1, 0, 0, 3, 2, 10, 166,
255, 245, 204, 62, 29, 47, 73, 152, 234, 245, 235, 223, 206,
243, 78],
[ 0, 168, 228, 40, 1, 4, 0, 0, 0, 0, 2, 2, 15,
222, 254, 113, 7, 0, 2, 0, 11, 186, 205, 22, 0, 44,
255, 100],
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94, 233, 249, 105, 64, 64, 54, 60, 181, 244, 100, 6, 53,
255, 109],
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6, 209, 255, 254, 255, 255, 251, 250, 254, 255, 252, 190, 138,
249, 104],
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0, 131, 255, 140, 80, 116, 234, 236, 143, 196, 248, 205, 252,
234, 52],
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26, 223, 196, 29, 0, 172, 249, 98, 11, 141, 242, 101, 219,
229, 4],
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99, 255, 79, 0, 132, 255, 128, 7, 0, 36, 50, 42, 222,
175, 0],
[ 80, 247, 81, 0, 2, 2, 2, 3, 3, 4, 7, 2, 1,
182, 235, 6, 55, 252, 168, 13, 0, 3, 0, 0, 73, 253,
103, 0],
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250, 249, 62, 188, 229, 55, 0, 3, 4, 0, 27, 200, 235,
22, 1],
[ 0, 158, 246, 70, 0, 8, 12, 18, 23, 49, 108, 176, 238,
228, 236, 196, 254, 137, 7, 4, 7, 5, 7, 131, 255, 109,
0, 1],
[ 1, 38, 246, 199, 140, 159, 183, 195, 201, 224, 246, 219, 152,
78, 97, 253, 255, 183, 48, 0, 2, 0, 82, 246, 174, 5,
4, 0],
[ 1, 0, 115, 255, 255, 250, 197, 177, 185, 174, 121, 42, 0,
0, 0, 122, 236, 255, 189, 46, 1, 74, 222, 220, 48, 2,
2, 0],
[ 0, 5, 0, 119, 213, 215, 127, 51, 5, 0, 0, 0, 0,
2, 2, 0, 97, 195, 251, 180, 122, 228, 224, 76, 0, 5,
0, 0],
[ 0, 0, 7, 0, 67, 192, 250, 236, 159, 92, 57, 35, 15,
2, 1, 16, 8, 120, 223, 255, 255, 201, 58, 0, 7, 0,
0, 0],
[ 0, 0, 0, 6, 0, 0, 71, 177, 239, 255, 246, 200, 157,
112, 108, 136, 184, 233, 242, 222, 166, 45, 0, 7, 0, 0,
0, 0],
[ 0, 0, 0, 0, 2, 4, 0, 0, 21, 82, 135, 193, 236,
244, 244, 240, 219, 158, 85, 13, 0, 0, 2, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 11,
45, 46, 25, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0,
0, 0]], dtype=uint8),
array([[ 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 39, 75, 59,
26, 4, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 1, 6, 0, 0, 110, 217, 195,
96, 13, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 1, 7, 0, 0, 114, 248, 255,
208, 123, 48, 3, 1, 1, 2, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 1, 7, 0, 0, 110, 244, 255,
236, 155, 63, 3, 0, 1, 2, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 1, 7, 0, 0, 111, 247, 254,
228, 138, 50, 2, 0, 1, 1, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 1, 7, 0, 0, 112, 250, 255,
224, 123, 37, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 1, 7, 0, 0, 114, 253, 255,
221, 108, 25, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 1, 7, 0, 0, 115, 255, 255,
217, 94, 12, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 1, 7, 0, 0, 116, 255, 255,
214, 79, 2, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 1, 7, 0, 0, 116, 255, 255,
210, 64, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 1, 7, 0, 0, 117, 255, 255,
205, 50, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 1, 7, 0, 0, 119, 255, 255,
199, 42, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 1, 7, 0, 0, 120, 255, 255,
194, 40, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 1, 7, 0, 0, 120, 255, 255,
205, 62, 1, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 1, 7, 0, 0, 121, 255, 255,
228, 147, 59, 4, 0, 2, 2, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 1, 7, 0, 0, 132, 255, 247,
223, 234, 145, 11, 0, 3, 3, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 1, 0, 5, 0, 14, 156, 255, 192,
168, 249, 213, 78, 13, 3, 1, 1, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 2, 0, 1, 0, 38, 181, 251, 138,
85, 186, 252, 204, 86, 0, 0, 5, 1, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 3, 0, 0, 5, 59, 196, 250, 130,
19, 54, 179, 255, 190, 59, 11, 5, 2, 3, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 4, 1, 3, 27, 108, 225, 242, 110,
0, 0, 81, 206, 255, 189, 65, 0, 0, 6, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 6, 0, 0, 63, 192, 249, 189, 65,
0, 1, 17, 70, 194, 255, 203, 90, 9, 0, 3, 5, 0,
0, 0],
[ 0, 0, 0, 2, 1, 5, 3, 33, 149, 255, 209, 81, 18,
1, 1, 0, 0, 55, 166, 255, 237, 118, 27, 0, 1, 4,
2, 0],
[ 0, 1, 1, 5, 0, 0, 36, 154, 245, 233, 116, 5, 0,
4, 1, 3, 4, 0, 25, 140, 236, 249, 161, 48, 1, 0,
0, 2],
[ 1, 2, 0, 0, 0, 50, 158, 254, 230, 113, 33, 1, 2,
3, 1, 2, 2, 4, 0, 28, 106, 212, 255, 205, 111, 30,
3, 0],
[ 0, 0, 0, 23, 87, 208, 255, 211, 99, 8, 0, 3, 2,
0, 0, 0, 0, 3, 2, 0, 4, 66, 158, 235, 255, 192,
69, 9],
[ 5, 23, 85, 181, 252, 242, 165, 46, 0, 1, 3, 0, 0,
0, 0, 0, 0, 0, 0, 5, 2, 0, 7, 74, 192, 223,
118, 32],
[ 26, 110, 235, 240, 177, 80, 11, 0, 3, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 3, 2, 0, 5, 11,
2, 0],
[ 0, 44, 96, 63, 0, 0, 3, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
1, 0]], dtype=uint8),
array([[ 66, 65, 64, 60, 53, 42, 26, 12, 2, 0, 0, 0, 1,
1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,
0, 0],
[196, 195, 195, 191, 175, 141, 94, 44, 6, 0, 0, 0, 2,
4, 0, 0, 1, 6, 8, 10, 7, 4, 0, 0, 0, 0,
0, 0],
[225, 222, 221, 213, 194, 155, 103, 50, 8, 0, 0, 0, 5,
25, 49, 86, 137, 188, 214, 202, 152, 79, 12, 0, 0, 0,
3, 10],
[224, 221, 217, 206, 183, 145, 96, 48, 9, 0, 0, 0, 9,
47, 93, 145, 198, 239, 250, 223, 163, 83, 13, 0, 0, 0,
3, 11],
[232, 226, 222, 208, 181, 141, 92, 46, 9, 0, 0, 0, 8,
46, 91, 142, 195, 235, 246, 218, 159, 81, 12, 0, 0, 0,
3, 11],
[240, 234, 225, 207, 176, 133, 84, 41, 7, 0, 0, 0, 5,
27, 57, 103, 164, 224, 255, 244, 189, 106, 27, 0, 0, 0,
2, 9],
[227, 223, 219, 208, 184, 145, 95, 47, 9, 0, 0, 0, 4,
20, 43, 85, 148, 214, 255, 251, 198, 117, 36, 0, 0, 0,
2, 8],
[185, 190, 200, 208, 205, 177, 123, 62, 10, 3, 1, 1, 5,
25, 49, 90, 152, 217, 255, 248, 195, 113, 33, 0, 0, 0,
2, 8],
[137, 156, 182, 213, 232, 218, 161, 82, 14, 0, 0, 1, 7,
24, 47, 89, 150, 215, 255, 249, 198, 116, 37, 0, 0, 0,
2, 7],
[ 62, 83, 128, 185, 238, 255, 222, 153, 78, 21, 4, 0, 0,
3, 19, 60, 127, 199, 248, 252, 213, 140, 63, 13, 2, 0,
1, 2],
[ 0, 0, 42, 103, 176, 237, 255, 247, 208, 160, 108, 62, 28,
13, 1, 24, 89, 167, 230, 255, 235, 177, 104, 49, 17, 4,
0, 0],
[ 3, 2, 12, 35, 71, 120, 173, 214, 240, 254, 253, 233, 194,
147, 111, 102, 127, 178, 228, 255, 249, 210, 151, 91, 46, 15,
0, 0],
[ 4, 1, 0, 0, 0, 6, 29, 62, 98, 132, 165, 194, 216,
237, 253, 255, 255, 255, 255, 255, 255, 252, 223, 168, 105, 53,
18, 4],
[ 0, 0, 0, 0, 1, 1, 3, 6, 1, 0, 0, 0, 9,
64, 134, 194, 230, 241, 241, 244, 254, 255, 255, 222, 171, 125,
93, 72],
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54, 114, 164, 193, 201, 201, 208, 230, 252, 255, 231, 194, 160,
134, 118],
[ 0, 0, 0, 0, 0, 0, 3, 5, 2, 0, 0, 0, 8,
55, 114, 163, 191, 201, 200, 204, 220, 239, 241, 220, 190, 163,
142, 129],
[ 0, 0, 0, 0, 0, 0, 3, 5, 2, 0, 0, 0, 8,
55, 113, 164, 193, 203, 203, 204, 216, 229, 228, 210, 185, 163,
148, 137],
[ 0, 0, 0, 0, 0, 0, 3, 5, 2, 0, 0, 0, 8,
54, 113, 163, 194, 204, 204, 203, 211, 219, 218, 203, 185, 168,
157, 150],
[ 0, 0, 0, 0, 0, 0, 3, 5, 2, 0, 0, 0, 8,
54, 113, 163, 194, 207, 205, 202, 206, 211, 208, 198, 185, 175,
168, 163],
[ 0, 0, 0, 0, 0, 0, 3, 5, 2, 0, 0, 0, 8,
54, 112, 163, 194, 208, 207, 202, 201, 202, 199, 192, 185, 180,
177, 174],
[ 0, 0, 0, 0, 0, 0, 3, 5, 2, 0, 0, 0, 8,
54, 112, 163, 196, 209, 208, 202, 196, 193, 189, 185, 185, 186,
187, 186],
[ 0, 0, 0, 0, 0, 0, 3, 5, 2, 0, 0, 0, 8,
54, 112, 163, 196, 210, 209, 201, 192, 185, 179, 179, 185, 192,
197, 199],
[ 0, 0, 0, 0, 0, 0, 3, 5, 2, 0, 0, 0, 8,
54, 112, 163, 196, 212, 211, 200, 187, 176, 170, 174, 185, 198,
207, 212],
[ 0, 0, 0, 0, 0, 0, 3, 5, 2, 0, 0, 0, 8,
54, 111, 164, 198, 214, 213, 199, 183, 167, 160, 167, 183, 202,
215, 223],
[ 0, 0, 0, 0, 0, 0, 3, 5, 2, 0, 0, 0, 8,
53, 109, 160, 193, 211, 210, 195, 177, 161, 155, 169, 194, 219,
236, 248],
[ 0, 0, 0, 0, 0, 0, 3, 5, 2, 0, 0, 0, 9,
56, 118, 173, 212, 227, 227, 206, 174, 142, 125, 133, 158, 188,
210, 224],
[ 0, 0, 0, 0, 0, 0, 3, 7, 3, 0, 0, 0, 10,
63, 129, 192, 239, 244, 244, 213, 152, 82, 25, 2, 8, 27,
44, 54],
[ 0, 0, 0, 0, 0, 0, 1, 3, 1, 0, 0, 0, 4,
26, 54, 82, 107, 122, 121, 103, 74, 37, 6, 1, 1, 0,
2, 5]], dtype=uint8),
array([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 61, 110, 118, 112, 53, 41, 83, 89, 27, 0, 0,
1, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0,
7, 111, 219, 251, 253, 254, 233, 230, 244, 254, 240, 196, 101,
14, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0,
16, 195, 231, 115, 40, 60, 208, 255, 183, 56, 143, 222, 241,
133, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0,
16, 188, 229, 87, 0, 0, 79, 143, 189, 139, 31, 120, 255,
170, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0,
8, 120, 248, 148, 0, 3, 7, 16, 115, 162, 23, 171, 242,
95, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0,
1, 36, 227, 206, 33, 1, 0, 0, 11, 37, 134, 255, 164,
14, 3],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 172, 240, 107, 0, 1, 0, 13, 119, 255, 196, 49,
0, 3],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2,
0, 1, 107, 236, 175, 0, 0, 19, 124, 255, 199, 57, 0,
3, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2,
0, 0, 71, 224, 205, 0, 0, 96, 237, 214, 28, 0, 1,
4, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2,
0, 0, 71, 224, 205, 0, 0, 101, 233, 217, 85, 42, 4,
0, 2],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 66, 224, 210, 0, 0, 18, 112, 225, 255, 209, 104,
17, 0],
[ 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1,
0, 0, 63, 223, 212, 0, 0, 3, 0, 32, 123, 219, 255,
134, 8],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 59, 220, 214, 0, 0, 5, 4, 0, 0, 38, 202,
219, 90],
[ 1, 18, 46, 60, 52, 17, 5, 4, 1, 0, 0, 0, 1,
0, 0, 62, 221, 213, 1, 0, 4, 0, 1, 3, 0, 122,
224, 160],
[ 68, 190, 252, 250, 254, 242, 195, 136, 79, 22, 0, 0, 3,
1, 1, 117, 237, 166, 0, 0, 4, 0, 1, 1, 0, 111,
213, 169],
[145, 223, 182, 75, 105, 154, 210, 247, 255, 245, 208, 134, 56,
5, 0, 184, 238, 98, 0, 0, 2, 0, 1, 1, 2, 109,
210, 168],
[164, 223, 123, 1, 8, 16, 26, 44, 87, 145, 204, 249, 242,
212, 209, 252, 175, 18, 1, 0, 1, 1, 2, 6, 0, 152,
234, 153],
[103, 224, 167, 0, 0, 0, 0, 0, 0, 13, 32, 61, 167,
255, 255, 210, 77, 0, 1, 1, 4, 10, 0, 0, 115, 246,
183, 32],
[ 16, 183, 255, 151, 57, 0, 0, 0, 0, 0, 0, 0, 38,
139, 169, 74, 9, 3, 0, 2, 0, 0, 23, 131, 252, 211,
54, 0],
[ 0, 50, 175, 255, 237, 193, 142, 83, 33, 0, 0, 0, 0,
13, 31, 1, 0, 2, 0, 2, 75, 154, 211, 255, 185, 64,
0, 3],
[ 2, 0, 28, 97, 149, 204, 240, 240, 214, 182, 152, 128, 71,
1, 0, 1, 8, 5, 8, 88, 212, 255, 194, 117, 34, 0,
1, 1],
[ 0, 4, 0, 0, 0, 31, 84, 135, 176, 208, 230, 249, 227,
134, 11, 0, 0, 0, 0, 168, 235, 122, 4, 0, 0, 2,
0, 0],
[ 0, 0, 3, 6, 3, 0, 0, 0, 0, 26, 52, 102, 227,
252, 29, 11, 63, 72, 76, 203, 234, 137, 50, 22, 5, 0,
0, 0],
[ 0, 0, 0, 0, 1, 3, 5, 6, 2, 0, 0, 45, 192,
241, 61, 124, 238, 255, 255, 248, 242, 246, 215, 125, 18, 0,
3, 0],
[ 0, 0, 0, 0, 0, 0, 0, 2, 1, 2, 36, 176, 255,
183, 49, 142, 200, 199, 217, 244, 187, 157, 221, 239, 53, 0,
6, 0],
[ 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 74, 216, 248,
134, 67, 39, 61, 91, 140, 252, 203, 126, 188, 253, 66, 0,
6, 0],
[ 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 112, 224,
244, 252, 255, 255, 252, 249, 240, 166, 179, 244, 159, 26, 0,
4, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 4,
46, 89, 99, 96, 98, 98, 89, 31, 52, 89, 3, 0, 2,
0, 0]], dtype=uint8),
...]
train_numpy_= np.asarray(train_data)
train_numpy_ = train_numpy_.reshape(train_numpy_.shape[0], train_numpy_.shape[1], train_numpy_.shape[2], 1)
train_numpy_ = train_numpy_.reshape(train_numpy_.shape[0], train_numpy_.shape[1], train_numpy_.shape[2], 1)
print(train_numpy_.shape)
(400000, 28, 28, 1)
test_data.head(100)
| data | target | |
|---|---|---|
| 219103 | [[0, 0, 4, 0, 60, 160, 235, 255, 254, 171, 35,... | 43 |
| 337616 | [[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 4, 36... | 67 |
| 209846 | [[0, 0, 0, 0, 1, 1, 2, 0, 0, 0, 7, 40, 89, 116... | 41 |
| 360752 | [[0, 0, 0, 0, 0, 33, 73, 78, 87, 96, 95, 95, 9... | 72 |
| 281345 | [[0, 1, 1, 2, 2, 3, 3, 2, 0, 0, 0, 4, 80, 128,... | 56 |
| ... | ... | ... |
| 226196 | [[35, 50, 73, 97, 101, 70, 28, 2, 0, 0, 2, 1, ... | 45 |
| 163246 | [[0, 0, 0, 0, 2, 4, 3, 1, 0, 0, 0, 0, 0, 0, 2,... | 32 |
| 261887 | [[0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0,... | 52 |
| 157632 | [[0, 45, 119, 115, 101, 39, 0, 1, 0, 0, 2, 0, ... | 31 |
| 147061 | [[0, 0, 0, 0, 1, 0, 1, 3, 0, 5, 8, 6, 4, 3, 3,... | 29 |
100 rows × 2 columns
test_images_data = test_data['data'].values
test_target_data = test_data['target'].values
test_images_data.shape
(100000,)
test_images_data
array([array([[ 0, 0, 4, 0, 60, 160, 235, 255, 254, 171, 35, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 6, 0, 94, 218, 238, 187, 149, 183, 241, 166, 4, 2,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 1, 0, 101, 250, 220, 96, 41, 23, 41, 177, 244, 21, 2,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 92, 239, 193, 48, 0, 0, 0, 0, 106, 251, 90, 1,
6, 4, 3, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 58, 232, 228, 97, 30, 15, 0, 19, 49, 128, 247, 191, 43,
0, 0, 0, 1, 3, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[100, 244, 202, 188, 183, 51, 26, 137, 194, 193, 240, 250, 150,
34, 22, 11, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0,
0, 0],
[ 22, 174, 246, 247, 255, 87, 132, 255, 243, 249, 253, 254, 255,
227, 171, 118, 34, 8, 2, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 9, 16, 78, 249, 129, 52, 153, 174, 166, 202, 255, 255,
255, 253, 247, 236, 180, 83, 16, 0, 0, 0, 1, 2, 0,
0, 0],
[ 0, 0, 0, 1, 213, 184, 16, 137, 254, 220, 215, 247, 143,
122, 191, 250, 255, 255, 237, 212, 199, 178, 118, 23, 0, 4,
2, 0],
[ 0, 3, 8, 0, 199, 202, 23, 42, 137, 188, 228, 251, 211,
120, 60, 39, 68, 125, 219, 255, 255, 247, 218, 193, 109, 0,
0, 3],
[ 0, 0, 5, 2, 198, 201, 32, 0, 4, 59, 170, 255, 224,
237, 170, 86, 14, 0, 43, 116, 183, 248, 255, 255, 226, 129,
17, 0],
[ 0, 0, 7, 0, 193, 208, 35, 4, 0, 6, 151, 237, 75,
163, 232, 255, 204, 108, 31, 0, 22, 123, 192, 230, 255, 255,
151, 31],
[ 0, 0, 7, 0, 192, 209, 36, 0, 2, 41, 208, 197, 0,
0, 52, 152, 227, 255, 209, 118, 49, 18, 65, 163, 208, 222,
231, 82],
[ 0, 0, 4, 1, 205, 191, 25, 0, 0, 50, 217, 161, 2,
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dtype=object)
test_target_data.shape
(100000,)
test_target_data
array([43, 67, 41, ..., 16, 34, 4], dtype=int64)
test_data = [x.reshape(28, 28, ) for x in test_images_data]
test_data
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...]
test_numpy_ = np.asarray(test_data)
test_numpy_ = test_numpy_.reshape(test_numpy_.shape[0], test_numpy_.shape[1], test_numpy_.shape[2], 1)
test_numpy_ = test_numpy_.reshape(test_numpy_.shape[0], test_numpy_.shape[1], test_numpy_.shape[2], 1)
test_numpy_.shape
(100000, 28, 28, 1)
Classifier used: Sequential
Convolution Layers: 2
Dense Layers: 3 (Input, Hidden, Output)
from tensorflow.keras.utils import to_categorical
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
#Initialising the CNN. We are using sequential classifier as we don't have multiple inputs and outputs
CNNModel = Sequential()
#1st Convolution Layer
CNNModel.add(Conv2D(32, (3, 3), activation = 'relu', padding='valid', input_shape = (28, 28, 1)))
#Downsampling the dataset using Pooling
CNNModel.add(MaxPooling2D(2, 2))
#Second Convolution Layer
CNNModel.add(Conv2D(64, (3, 3), activation="relu", padding='valid'))
CNNModel.add(MaxPooling2D(2, 2))
#Flattening
CNNModel.add(Flatten())
#Adding Input layer
CNNModel.add(Dense(units = 512, activation = 'relu', kernel_initializer='glorot_uniform'))
#Adding Hidden Layers
CNNModel.add(Dense(units = 256, activation = 'relu', kernel_initializer='glorot_uniform'))
#CNNModel.add(Dense(units = 256, activation = 'relu', kernel_initializer='glorot_uniform'))
#Adding Output layer
CNNModel.add(Dense(units = 100, activation = 'softmax'))
CNNModel.compile(optimizer = tf.keras.optimizers.Adam(learning_rate = 0.001), loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])
CNNModel.fit(train_numpy_, train_target_data, epochs = 20, validation_data = (test_numpy_, test_target_data), batch_size=128)
Epoch 1/20 3125/3125 [==============================] - 283s 90ms/step - loss: 2.2062 - accuracy: 0.4813 - val_loss: 1.6560 - val_accuracy: 0.5758 Epoch 2/20 3125/3125 [==============================] - 287s 92ms/step - loss: 1.5145 - accuracy: 0.6091 - val_loss: 1.4671 - val_accuracy: 0.6208 Epoch 3/20 3125/3125 [==============================] - 291s 93ms/step - loss: 1.3498 - accuracy: 0.6466 - val_loss: 1.4097 - val_accuracy: 0.6342 Epoch 4/20 3125/3125 [==============================] - 293s 94ms/step - loss: 1.2426 - accuracy: 0.6708 - val_loss: 1.4046 - val_accuracy: 0.6393 Epoch 5/20 3125/3125 [==============================] - 285s 91ms/step - loss: 1.1542 - accuracy: 0.6904 - val_loss: 1.3940 - val_accuracy: 0.6447 Epoch 6/20 3125/3125 [==============================] - 284s 91ms/step - loss: 1.0753 - accuracy: 0.7079 - val_loss: 1.4307 - val_accuracy: 0.6401 Epoch 7/20 3125/3125 [==============================] - 290s 93ms/step - loss: 1.0038 - accuracy: 0.7236 - val_loss: 1.4823 - val_accuracy: 0.6356 Epoch 8/20 3125/3125 [==============================] - 281s 90ms/step - loss: 0.9384 - accuracy: 0.7386 - val_loss: 1.5333 - val_accuracy: 0.6355 Epoch 9/20 3125/3125 [==============================] - 294s 94ms/step - loss: 0.8825 - accuracy: 0.7510 - val_loss: 1.6260 - val_accuracy: 0.6302 Epoch 10/20 3125/3125 [==============================] - 290s 93ms/step - loss: 0.8322 - accuracy: 0.7628 - val_loss: 1.6685 - val_accuracy: 0.6270 Epoch 11/20 3125/3125 [==============================] - 284s 91ms/step - loss: 0.7848 - accuracy: 0.7737 - val_loss: 1.7067 - val_accuracy: 0.6253 Epoch 12/20 3125/3125 [==============================] - 287s 92ms/step - loss: 0.7466 - accuracy: 0.7834 - val_loss: 1.8018 - val_accuracy: 0.6167 Epoch 13/20 3125/3125 [==============================] - 293s 94ms/step - loss: 0.7062 - accuracy: 0.7930 - val_loss: 1.8979 - val_accuracy: 0.6192 Epoch 14/20 3125/3125 [==============================] - 287s 92ms/step - loss: 0.6723 - accuracy: 0.8011 - val_loss: 2.0265 - val_accuracy: 0.6136 Epoch 15/20 3125/3125 [==============================] - 287s 92ms/step - loss: 0.6402 - accuracy: 0.8096 - val_loss: 2.0541 - val_accuracy: 0.6131 Epoch 16/20 3125/3125 [==============================] - 206s 66ms/step - loss: 0.6150 - accuracy: 0.8160 - val_loss: 2.1809 - val_accuracy: 0.6073 Epoch 17/20 3125/3125 [==============================] - 140s 45ms/step - loss: 0.5891 - accuracy: 0.8230 - val_loss: 2.1895 - val_accuracy: 0.6031 Epoch 18/20 3125/3125 [==============================] - 137s 44ms/step - loss: 0.5693 - accuracy: 0.8284 - val_loss: 2.3517 - val_accuracy: 0.5991 Epoch 19/20 3125/3125 [==============================] - 146s 47ms/step - loss: 0.5482 - accuracy: 0.8343 - val_loss: 2.3987 - val_accuracy: 0.6012 Epoch 20/20 3125/3125 [==============================] - 140s 45ms/step - loss: 0.5312 - accuracy: 0.8389 - val_loss: 2.4240 - val_accuracy: 0.5925
<keras.callbacks.History at 0x19b7c294f40>
CNNModel.summary()
Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_2 (Conv2D) (None, 26, 26, 32) 320
max_pooling2d_2 (MaxPooling (None, 13, 13, 32) 0
2D)
conv2d_3 (Conv2D) (None, 11, 11, 64) 18496
max_pooling2d_3 (MaxPooling (None, 5, 5, 64) 0
2D)
flatten_1 (Flatten) (None, 1600) 0
dense_3 (Dense) (None, 512) 819712
dense_4 (Dense) (None, 256) 131328
dense_5 (Dense) (None, 100) 25700
=================================================================
Total params: 995,556
Trainable params: 995,556
Non-trainable params: 0
_________________________________________________________________
model_nikkulk=CNNModel.save('model_nikkulk')
INFO:tensorflow:Assets written to: model_nikkulk\assets